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Enregistrement W2800666481 · doi:10.2118/190083-ms

Recovery Improvement by Chemical Additives to Steam Injection: Identifying Underlying Mechanisms Through Core and Visual Experiments

2018· article· en· W2800666481 sur OpenAlex
Fritjof Bruns, Tayfun Babadagli

Pourquoi ce travail est dans la base

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fundUn bailleur canadien est enregistré sur le travail.

Notice bibliographique

RevueSPE Western Regional Meeting · 2018
Typearticle
Langueen
DomaineEngineering
ThématiqueEnhanced Oil Recovery Techniques
Établissements canadiensUniversity of Alberta
Organismes subventionnairesNatural Sciences and Engineering Research Council of Canada
Mots-clésSteam injectionEnhanced oil recoveryPetroleum engineeringEnvironmental scienceProcess engineeringWaste managementChemistryMaterials scienceChemical engineeringGeologyEngineering

Résumé

récupéré en direct d'OpenAlex

Abstract Steam injection of any kind (flooding, cyclic, or gravity drainage) is a proven heavy-oil recovery method; however, it also involves excessive costs due energy and water needed for steam generation. Any effort in reducing this cost or improving oil recovery is essential for sustainable production, especially in times of low oil prices. Chemical additives to steam were suggested a few decades ago to improve two major mechanisms, namely heat transfer and interfacial phenomena, but research in that area discontinued due to the cost and thermal stability problem of the additive chemicals. With recent advancements in nano-technologies, new generation chemicals showed potential to reconsider chemical additives to improve the efficiency of steam injection. This, however, requires extensive research especially for mechanism identification. The objective of this paper is to identify the flow characteristics and the mechanisms involved in recovery enhancement by chemical additives through core and visual tests. To mimic the gravity assisted drainage and flooding type steam displacement tests we performed previously (Bruns and Babadagli 2017) on cores saturated with 27,000 heavy-crude-oil, a visual Hele-Shaw model was designed to simulate the same process and identify the physical characteristics of the steam-condensate-oil interface and the role played by added chemicals. Majority of the chemicals/chemical blends showed either improvement in the rate or ultimate recoveries in the coreflooding tests and, based on this data, the best performing and the most thermally stable chemicals were selected for the visual tests. These chemicals include ionic liquids, internal olefin sulfonate, biodiesel (thermally stable surface active agents) and solvents (heptane), and nano-fluids (silicon oxide). The chemical solution was injected at constant rate and pressure after being vaporized in an oven along with steam and the whole process was recorded with a camera. The contribution to recovery improvement through these phenomena in flooding and gravity controlled cases were identified. Foaming, emulsification, and IFT reduction yielding reduced drag forces between two phases at the interface were observed to be the main reason for positive contribution of chemicals. Biodiesel (Surfactant 1) exhibited a diffusion-like behavior near the injection port where no residual oil was noticed. The solvent (heptane), simulating ES-SAGD, stabilized the flow of steam in the late stage of the experiment due to the viscosity reduction. Improved oil + condensate drainage was assumed to be the contributing mechanism because of the change in surface properties during the injection of the ionic liquid. Nanoparticle, silicon oxide, and the internal olefin sulfonate (Surfactant 2) showed similar improvements in tip-splitting of the displacing fingers. It was concluded that the interfacial tension (IFT) reduction resulted in a wider occupation of the Hele-Shaw cell (better lateral sweep).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,089
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,046
Tête enseignante GPT0,324
Écart entre enseignants0,278 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle